ma206data package demo

library(tidyverse)
# remotes::install_github("EvaMaeRey/ma206data")

library(ma206data)
options(scipen = 10)

prelim_NationalAnthemTimes %>% 
  ggplot() + 
  aes(x = year) + 
  aes(y = time) +
  geom_point() + 
  aes(color = sex) + 
  aes(shape = genre) 

chap2_LaughIncrease %>% 
  ggplot() + 
  aes(x = rating_increase) +
  geom_dotplot() + 
  geom_vline(xintercept = 0,
             linetype = "dashed")

## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.

chap3_Hockey2 %>% 
  ggplot() + 
  aes(x = margin_victory %>% as.factor()) + 
  geom_bar() 

chap5_Blood %>% 
  ggplot() + 
  aes(x = year %>% as.factor(), fill = response) + 
  geom_bar(position = "fill")

chap5_Gilbert %>% 
  ggplot() + 
  aes(x = gilbert_worked, fill = patient) + 
  geom_bar(position = "fill")

chap6_DungBeetles %>% 
  ggplot() +
  aes(color = cap, x = time) + 
  geom_point(y = 0, alpha = .7) + 
  geom_density(alpha = .2, aes(fill = cap)) + 
  facet_wrap(~cap, ncol = 1) + 
  ggxmean::geom_x_mean()

chap7_DadJokes %>% 
  pivot_longer(-1) %>% 
  mutate(name = fct_rev(name)) %>% 
  arrange(joke, name) %>% 
  group_by(joke) %>% 
  mutate(outcome_diff = lead(value) - value) %>% 
  ggplot() +
  aes(x = name, y = value) + 
  geom_point() +
  geom_line(aes(x = name %>% 
                  as.factor() %>%  
                  as.numeric())) +
  aes(group = joke) + 
  geom_line(aes(color = outcome_diff > 0))

chap7_DadJokes %>% 
  mutate(diff = laugh_track - no_laugh_track) %>% 
  ggplot() + 
  aes(y = joke, x = no_laugh_track, color = diff > 0) + 
  geom_point(color = "cadetblue") + 
  geom_segment(aes(xend = laugh_track, 
                                   yend = joke), arrow = arrow(length = unit(0.20,"cm"))) + 
  geom_point(color = "cadetblue4", aes(x = laugh_track))

chap8_Goals %>% 
  count(gender, goal) %>% 
  ggplot() + 
  aes(x = gender, y = n, fill = goal) + 
  geom_col(position = "fill")

chap8_Goals %>% 
  count(gender, goal) %>% 
  group_by(gender) %>% 
  mutate(percent_within_gender = 100*n/sum(n)) %>% 
  ggplot() + 
  aes(x = gender, label = n, fill = percent_within_gender, y  = goal) + 
  geom_text() + 
  geom_tile(alpha = .6) + 
  scale_y_discrete(limits = rev)

chap9_Donation %>% 
  ggplot() + 
  aes(x = state, y = donation) + 
  geom_jitter(width = .1) + 
  ggxmean::geom_xy_means(color = "goldenrod", size = 5)

chap10_DraftLottery %>% 
  ggplot() + 
  aes(x = sequential_date, y = draft_number) + 
  geom_point() + 
  geom_density_2d_filled(alpha = .4) + 
  ggxmean:::geom_corrlabel()

chap10_DraftLottery %>% 
  ggplot() + 
  aes(x = sequential_date, 
      y = sample(draft_number)) + # true non association by random reorder
  geom_point() + 
  geom_density_2d_filled(alpha = .4) + 
  ggxmean:::geom_corrlabel()

chap9_Comprehension %>% 
  ggplot() + 
  aes(x = condition, y = comprehension) + 
  geom_jitter(width = .1) + 
  ggxmean:::geom_xy_means(color = "goldenrod",
                          shape = "-", size = 20)

chap10_WimbledonMF %>% 
  ggplot() + 
  aes(x = year, y = height_cm, color = sex) + 
  geom_point(alpha = .5) + 
  geom_smooth(method = lm)

## `geom_smooth()` using formula 'y ~ x'